Are They Created Equal? A Relative Weights Analysis of the Contributions of Job Demands and Resources to Well-Being and Turnover Intention
Bibliographic record
Abstract
Building upon the Job Demands-Resources (JD-R) model (Demerouti et al., 2001) and the extensive research on employee turnover intention and well-being, we examined various demands and resources in relation to these outcomes. This study examined the differential relationship between job demands, and personal and job resources, and two organizational outcomes: turnover intention and emotional exhaustion. The job demands were role overload, role conflict, role ambiguity, and work-life balance. The job resources were resilience, servant leadership, relatedness, autonomy, job opportunities, pay satisfaction, and person-organization fit. An online questionnaire was administered to full-time employees via Qualtrics panel ( N = 364). Job demands were positively related to emotional exhaustion, and personal and job resources were negatively related to turnover intention. Using relative weights analysis, demands and resources were found to account for different amounts of variance in the outcome variables. This study informs our understanding of and contributes to the advancement of the JD-R model to encompass various job demands and personal and job resources and their differential relationship to emotional exhaustion and turnover intention.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".